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Feedback Research in Second Language 

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Artificial Intelligence Knowledge Development

The rapid adoption of generative artificial intelligence (AI) has transformed academic writing while creating new challenges for ethical source use, academic integrity, and responsible engagement with scholarly sources. This qualitative descriptive study explored the challenges advanced English learners face in using sources ethically and avoiding plagiarism in AI-mediated academic writing. Data were collected from 75 multilingual learners enrolled in an advanced academic writing course across three consecutive semesters. Multiple sources of evidence, including essays, writing drafts, reflection papers, instructor feedback, and classroom observations, were analyzed using Braun and Clarke&rsquo;s (2023) Reflexive Thematic Analysis. The findings revealed five interconnected challenges related to citation practices, authorship identification, source verification, AI-generated references, and ethical decision-making. Together, these findings suggest that ethical source use extends beyond citation accuracy and requires the integration of academic, information, digital, and AI literacies. The study further proposes epistemic responsibility as a conceptual lens for understanding ethical source use in AI-mediated writing and offers pedagogical implications for fostering responsible AI-assisted writing, critical source evaluation, and informed scholarly decision-making among multilingual learners.

frsl.2027.05.01.pdf

Original Research 

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Ethical Source Use in the Age of AI: Challenges Faced by Advanced English Learners in Academic Writing

The widespread adoption of generative artificial intelligence (AI) has prompted renewed attention to how academic writing should be assessed in higher education. While AI supports multilingual writers throughout the writing process, it also challenges traditional product-based assessment by obscuring students' learning, authorship, and decision-making. Guided by Sociocultural Theory, this qualitative descriptive study explored how assessment can be redesigned for AI-mediated academic writing. Data included classroom reflections, student writing artifacts, multiple drafts, revision records, and AI-supported writing activities. Data was analyzed using Braun and Clarke&rsquo;s (2023) Reflexive Thematic Analysis. Thematic analysis generated six interconnected themes that reconceptualize assessment as the documentation of learning rather than the evaluation of final products alone. The findings suggest that process-oriented assessment, including scaffolded drafting, revision histories, reflective writing, AI-use disclosures, and ongoing feedback, provides richer evidence of students' cognitive engagement, ethical source use, and writing development. The study proposes a process-oriented assessment framework that promotes transparency, responsible AI use, learner accountability, and more authentic assessment in multilingual writing classrooms.

ltf.2026.14.01.pdf

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Rethinking Assessment in the AI Era: Emerging Challenges and Responses